AI training method helps robots carry lab-learned skills into real-world tasks
Robots are trained for specific tasks, such as cutting, using simulation. However, collecting real-world data is expensive, slow, and sometimes unsafe, particularly for tasks involving physical interaction. A new AI-based method co-developed by Aston University’s Dr. Alireza Rastegarpanah could revolutionize the way advanced robotic systems are trained for real-life tasks, making them more practical and reliable.
A collaboration between NVIDIA and academic researchers is prepping robots for surgery. ORBIT-Surgical — developed by researchers from the University of Toronto, UC Berkeley, ETH Zurich, Georgia Tech and NVIDIA — is a simulation framework to train robots that could augment the skills of surgical teams while reducing surgeons’ cognitive…
To tackle different real-world tasks, robots should be able to handle and manipulate a variety of objects and materials, including paper. While roboticists have successfully improved the ability of humanoid robots or robotic grippers to handle several materials, paper folding remains a rarely explored topic within the robotics community.
Imagine having to straighten up a messy kitchen, starting with a counter littered with sauce packets. If your goal is to wipe the counter clean, you might sweep up the packets as a group. If, however, you wanted to first pick out the mustard packets before throwing the rest away,…